add datasource local and result detail

app.py

@@ -1,22 +1,37 @@
 import json
+import os
+import shutil
 
 import gradio as gr
 from dingo.exec import Executor
 from dingo.io import InputArgs
 
 
-def dingo_demo(input_path, data_format, column_content, rule_list, prompt_list, model, key, api_url):
-    if not input_path:
-        return 'ValueError: input_path can not be empty, please input.'
+def dingo_demo(dataset_source, input_path, uploaded_file, data_format, column_content, rule_list, prompt_list, model,
+               key, api_url):
     if not data_format:
-        return 'ValueError: data_format can not be empty, please input.'
+        return 'ValueError: data_format can not be empty, please input.', None
     if not column_content:
-        return 'ValueError: column_content can not be empty, please input.'
+        return 'ValueError: column_content can not be empty, please input.', None
     if not rule_list and not prompt_list:
-        return 'ValueError: rule_list and prompt_list can not be empty at the same time.'
+        return 'ValueError: rule_list and prompt_list can not be empty at the same time.', None
+
+    # Handle input path based on dataset source
+    if dataset_source == "hugging_face":
+        if not input_path:
+            return 'ValueError: input_path can not be empty for hugging_face dataset, please input.', None
+        final_input_path = input_path
+    else:  # local
+        if not uploaded_file:
+            return 'ValueError: Please upload a file for local dataset.', None
+        final_input_path = uploaded_file.name
 
     input_data = {
-        "input_path": input_path,
+        "dataset": dataset_source,
+        "input_path": final_input_path,
+        "output_path": "" if dataset_source == 'hugging_face' else os.path.dirname(final_input_path),
+        "save_data": True,
+        "save_raw": True,
         "data_format": data_format,
         "column_content": column_content,
         "custom_config":
@@ -36,9 +51,33 @@ def dingo_demo(input_path, data_format, column_content, rule_list, prompt_list,
     }
     input_args = InputArgs(**input_data)
     executor = Executor.exec_map["local"](input_args)
-    result = executor.execute()
-    summary = result[0].to_dict()
-    return json.dumps(summary, indent=4)
+    executor.execute()
+    summary = executor.get_summary().to_dict()
+    detail = executor.get_bad_info_list()
+    new_detail = []
+    for item in detail:
+        new_detail.append(item.to_raw_dict())
+    if summary['output_path']:
+        shutil.rmtree(summary['output_path'])
+
+    # 返回两个值：概要信息和详细信息
+    return json.dumps(summary, indent=4), new_detail
+
+
+def update_input_components(dataset_source):
+    # 根据数据源的不同，返回不同的输入组件
+    if dataset_source == "hugging_face":
+        # 如果数据源是huggingface，返回一个可见的文本框和一个不可见的文件组件
+        return [
+            gr.Textbox(visible=True),
+            gr.File(visible=False),
+        ]
+    else:  # local
+        # 如果数据源是本地，返回一个不可见的文本框和一个可见的文件组件
+        return [
+            gr.Textbox(visible=False),
+            gr.File(visible=True),
+        ]
 
 
 if __name__ == '__main__':
@@ -51,24 +90,76 @@ if __name__ == '__main__':
         gr.HTML(header)
         with gr.Row():
             with gr.Column():
-                input_path = gr.Textbox(value='chupei/format-jsonl', placeholder="please input huggingface dataset path", label="input_path")
-                data_format = gr.Dropdown(["jsonl", "json", "plaintext", "listjson"], label="data_format")
-                column_content = gr.Textbox(value="content", placeholder="please input column name of content in dataset", label="column_content")
-                rule_list = gr.CheckboxGroup(choices=rule_options, label="rule_list")
-                prompt_list = gr.CheckboxGroup(choices=prompt_options, label="prompt_list")
-                model = gr.Textbox(placeholder="If want to use llm, please input model, such as: deepseek-chat", label="model")
-                key = gr.Textbox(placeholder="If want to use llm, please input key, such as: 123456789012345678901234567890xx", label="key")
-                api_url = gr.Textbox(placeholder="If want to use llm, please input api_url, such as: https://api.deepseek.com/v1", label="api_url")
+                with gr.Column():
+                    dataset_source = gr.Dropdown(
+                        choices=["hugging_face", "local"],
+                        value="local",
+                        label="dataset [source]"
+                    )
+                    input_path = gr.Textbox(
+                        value='chupei/format-jsonl',
+                        placeholder="please input hugging_face dataset path",
+                        label="input_path",
+                        visible=False
+                    )
+                    uploaded_file = gr.File(
+                        label="upload file",
+                        visible=True
+                    )
+
+                    data_format = gr.Dropdown(
+                        ["jsonl", "json", "plaintext", "listjson"],
+                        label="data_format"
+                    )
+                    column_content = gr.Textbox(
+                        value="content",
+                        placeholder="please input column name of content in dataset",
+                        label="column_content"
+                    )
+
+                    rule_list = gr.CheckboxGroup(
+                        choices=rule_options,
+                        label="rule_list"
+                    )
+                    prompt_list = gr.CheckboxGroup(
+                        choices=prompt_options,
+                        label="prompt_list"
+                    )
+                    model = gr.Textbox(
+                        placeholder="If want to use llm, please input model, such as: deepseek-chat",
+                        label="model"
+                    )
+                    key = gr.Textbox(
+                        placeholder="If want to use llm, please input key, such as: 123456789012345678901234567890xx",
+                        label="API KEY"
+                    )
+                    api_url = gr.Textbox(
+                        placeholder="If want to use llm, please input api_url, such as: https://api.deepseek.com/v1",
+                        label="API URL"
+                    )
+
                 with gr.Row():
                     submit_single = gr.Button(value="Submit", interactive=True, variant="primary")
+
             with gr.Column():
-                # 输出组件
-                output = gr.Textbox(label="output")
+                # 修改输出组件部分，使用Tabs
+                with gr.Tabs():
+                    with gr.Tab("Result Summary"):
+                        summary_output = gr.Textbox(label="summary", max_lines=50)
+                    with gr.Tab("Result Detail"):
+                        detail_output = gr.JSON(label="detail", max_height=800)  # 使用JSON组件来更好地展示结构化数据
+
+        dataset_source.change(
+            fn=update_input_components,
+            inputs=dataset_source,
+            outputs=[input_path, uploaded_file]
+        )
 
         submit_single.click(
             fn=dingo_demo,
-            inputs=[input_path, data_format, column_content, rule_list, prompt_list, model, key, api_url],
-            outputs=output
+            inputs=[dataset_source, input_path, uploaded_file, data_format, column_content, rule_list, prompt_list,
+                    model, key, api_url],
+            outputs=[summary_output, detail_output]  # 修改输出为两个组件
         )
 
     # 启动界面